Contents

1 QC by total umi count, number of features detected, and percentage of mitochondrial DNA

This notebook performs outlier-based QC for the non-genotyped large base editor screen, removing low outliers for the total count, low outliers for the number of detected features and high outliers for the percentage of counts from mitochondrial genes.

knitr::opts_chunk$set(
    echo = FALSE,
    message = FALSE,
    warning = FALSE,
    message = FALSE,
    dev = "pdf",
    dpi=300
)

library(Matrix)
library(ggplot2)
library(AnnotationHub)
library(AnnotationDbi)
library(scran)
library(scater)
library(grateful)
library(gridExtra)

folders_cellranger <- c("../cellranger/cellranger700_count_46539_7105STDY13259924_GRCh38-2020-A","../cellranger/cellranger700_count_46539_7105STDY13259926_GRCh38-2020-A")
file_name_mRNA_matrices <- "mRNA_matrices.rds"
file_name_mRNA_matrices_QC <- "mRNA_matrices_QC.rds"
file_name_sces <- "sce_list.rds"
ids <- c("CBE","ABE")

source("../../core_functions.R")

Number of cells before QC: 58673 In the ABE data set: 30226 In the CBE data set: 28447

Number of cells after QC: 56220 In the ABE data set: 28718 In the CBE data set: 27502

2 Creating SingleCellExperiment and Normalisation